R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

SPRoBERTa: protein embedding learning with local fragment modeling.

Well understanding protein function and structure in computational biology helps in the understanding of human beings. To face the limited proteins that are annotated structurally and functionally, the scientific community embraces the self-supervised pre-training methods from large amounts of unlabeled protein sequences for protein embedding learning. However, the protein is usually represented by individual amino acids with limited vocabulary size (e.g. 20 type proteins), without considering the strong local semantics existing in protein sequences. In this work, we propose a novel pre-training modeling approach SPRoBERTa. We first present an unsupervised protein tokenizer to learn protein representations with local fragment pattern. Then, a novel framework for deep pre-training model is introduced to learn protein embeddings. After pre-training, our method can be easily fine-tuned for different protein tasks, including amino acid-level prediction task (e.g. secondary structure prediction), amino acid pair-level prediction task (e.g. contact prediction) and also protein-level prediction task (remote homology prediction, protein function prediction). Experiments show that our approach achieves significant improvements in all tasks and outperforms the previous methods. We also provide detailed ablation studies and analysis for our protein tokenizer and training framework.

Associations between the TyG index and the ɑ-Klotho protein in middle-aged and older population relevant to diabetes mellitus in NHANES 2007-2016.

BACKGROUND: The anti-aging protein Klotho has diverse functions in antioxidative stress and energy metabolism through several pathways. While it has been reported that α-Klotho is downregulated in patients with insulin resistance (IR), the association between Klotho and IR is complex and controversial. The triglyceride-glucose (TyG) index has provided a practical method for assessing IR. With this in mind, our study aimed to investigate the relationship between the TyG index and soluble α-Klotho protein levels in US populations, both with and without diabetes mellitus. METHODS: This cross-sectional study analyzed data from middle-aged and older participants in the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2016. The participants were divided into two groups based on their diabetes mellitus status: those with diabetes and those without diabetes. To evaluate the relationship between the TyG index and the concentration of the α-Klotho protein in each group, a series of survey-weighted multivariable linear regression models were employed. Furthermore, to examine the association between these two variables, multivariable-adjusted restricted cubic spline curves and subgroup analysis were generated. RESULTS: The study involved 6,439 adults aged 40 years or older, with a mean age of 57.8 ± 10.9 years. Among them, 1577 (24.5%) had diabetes mellitus. A subgroup analysis indicated that the presence of diabetes significantly affected the relationship between the TyG index and the α-Klotho level. After considering all covariables, regression analysis of the participants without diabetes revealed that the α-Klotho concentration decreased by 32.35 pg/ml (95% CI: -50.07, -14.64) with each one unit increase in TyG (p < 0.001). The decline in α-Klotho levels with elevated TyG was more pronounced in the female population. In patients with diabetes mellitus, a non-linear association between the TyG index and α-Klotho was observed. There was no significant correlation observed between the two when TyG index were below 9.7. However, there was an increase in klotho levels of 106.44 pg/ml for each unit increase in TyG index above 9.7 (95% CI: 28.13, 184.74) (p = 0.008). CONCLUSION: Our findings suggested that the presence of diabetes may influence the relationship between the TyG index and soluble α-Klotho. Furthermore, there seem to be sex differences in individuals without diabetes. Further studies are necessary to validate these findings.

Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.

A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

Rapid Formation of Intramolecular Disulfide Bridges using Light: An Efficient Method to Control the Conformation and Function of Bioactive Peptides.

Disulfide bonds are widely found in natural peptides and play a pivotal role in stabilizing their secondary structures, which are highly associated with their biological functions. Herein, we introduce a light-mediated strategy to effectively control the formation of disulfides. Our strategy is based on 2-nitroveratryl (oNv), a widely used photolabile motif, which serves both as a photocaging group and an oxidant (after photolysis). We demonstrated that irradiation of oNv-caged thiols with UV light could release free thiols that are rapidly oxidized by locally released byproduct nitrosoarene, leading to a "break-to-bond" fashion. This strategy is highlighted by the in situ restoration of the antimicrobial peptide tachyplesin I (TPI) from its external disulfide-caged analogue TPI-1. TPI-1 exhibits a distorted structure and a diminished function. However, upon irradiation, the ß-hairpin structure and membrane activity of TPI were largely restored via rapid intramolecular disulfide formation. Our study proposes a powerful method to regulate the conformation and function of peptides in a spatiotemporal manner, which has significant potential for the design of disulfide-centered light-responsive systems.

Back translation for molecule generation.

MOTIVATION: Molecule generation, which is to generate new molecules, is an important problem in bioinformatics. Typical tasks include generating molecules with given properties, molecular property improvement (i.e. improving specific properties of an input molecule), retrosynthesis (i.e. predicting the molecules that can be used to synthesize a target molecule), etc. Recently, deep-learning-based methods received more attention for molecule generation. The labeled data of bioinformatics is usually costly to obtain, but there are millions of unlabeled molecules. Inspired by the success of sequence generation in natural language processing with unlabeled data, we would like to explore an effective way of using unlabeled molecules for molecule generation. RESULTS: We propose a new method, back translation for molecule generation, which is a simple yet effective semisupervised method. Let X be the source domain, which is the collection of properties, the molecules to be optimized, etc. Let Y be the target domain which is the collection of molecules. In particular, given a main task which is about to learn a mapping from the source domain X to the target domain Y, we first train a reversed model g for the Y to X mapping. After that, we use g to back translate the unlabeled data in Y to X and obtain more synthetic data. Finally, we combine the synthetic data with the labeled data and train a model for the main task. We conduct experiments on molecular property improvement and retrosynthesis, and we achieve state-of-the-art results on four molecule generation tasks and one retrosynthesis benchmark, USPTO-50k. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/fyabc/BT4MolGen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovering drug-target interaction knowledge from biomedical literature.

MOTIVATION: The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction, becomes an urgent demand in the industry. Existing methods of discovering biological knowledge are mainly extractive approaches that often require detailed annotations (e.g. all mentions of biological entities, relations between every two entity mentions, etc.). However, it is difficult and costly to obtain sufficient annotations due to the requirement of expert knowledge from biomedical domains. RESULTS: To overcome these difficulties, we explore an end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations. Further, we propose a semi-supervised method, which leverages the aforementioned end-to-end model to filter unlabeled literature and label them. Experimental results show that our method significantly outperforms extractive baselines on DTI discovery. We also create a dataset, KD-DTI, to advance this task and release it to the community. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/bert-nmt/BERT-DTI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glucose-Triggered Release of trans-N-p-Coumaroyltyramine from Zeolitic Imidazolate Framework-8 (ZIF8) Modified with Dioscorea opposita Thunb Polysaccharide.

A Dioscorea opposita Thunb polysaccharide (DOP)-modified ZIF8 material was developed in this study, which can be used as a "smart" glucose-responsive carrier to control the slow release of drugs. The 3-aminophenylboronic acid (APBA) functionalized carboxylated long-chain polymer poly(ethylene glycol) (PEG) segments, which were first modified on the surface of ZIF8 nanoparticles with a hydrogen bond and then chemically cross-linked with DOP through a borate ester bond, leading to the drugs loaded on ZIF8 being "closed" in PBS but being "open" via taking off the DOP coating in high concentrations of glucose; thus, leakage can be prevented in the drug loaded and a glucose-triggered release can effectively result. Moreover, the materials showed good biocompatibility and the released trans-N-p-coumaroyltyramine (NCT) could work synergistically with the DOP to improve insulin resistance and promote glucose consumption in insulin-resistant HepG2 cells.

Relationship between the triglyceride-glucose index and coronary artery calcification in asymptomatic, non-diabetic patients undergoing maintenance hemodialysis.

OBJECTIVE: Coronary artery calcification (CAC) is positively and independently associated with cardiovascular disease (CVD) in patients undergoing maintenance hemodialysis (MHD). Insulin resistance is independently associated with CAC and is an important risk factor for CVD. The triglyceride-glucose (TyG) index is a reliable biomarker of insulin resistance. This cross-sectional, observational study aimed to investigate the relationship between the TyG index and CAC in asymptomatic non-diabetic patients undergoing MHD. METHODS: The quantitative coronary artery calcification score (CACS) was calculated and expressed using the Agatston score. The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multiple Poisson regression analysis, Spearman correlation analysis, and receiver operating characteristic (ROC) curves were used to investigate the relationship between the TyG index and CAC. RESULTS: The 151 patients were divided into three groups according to the tertiles of the TyG index. With an increase in the TyG index, the CACS significantly increased (Spearman's rho = 0.414, p < 0.001). Poisson regression analysis indicated that the TyG index was independently related to the presence of CAC (prevalence ratio, 1.281 [95% confidence interval, 1.121-1.465], p < 0.001). Furthermore, ROC curve analysis showed that the TyG index was of value in predicting the CAC in asymptomatic non-diabetic patients undergoing MHD, with an area under the curve of 0.667 (p = 0.010). CONCLUSION: The TyG index is independently related to the presence of CAC in asymptomatic, non-diabetic patients undergoing MHD.

TiO2 nanotube immobilised 5-lipoxygenase-mediated screening and isolation of anti-inflammatory active compounds from the leaves of lonicera japonica thunb.

In this work, a highly effective separation approach mediated by 5-Lipoxygenase (5-LOX) was established for screening and isolation of anti-inflammatory ingredients from leaves of Lonicera japonica Thunb. (LLJT). Using 5-LOX immobilised on TiO2 nanotubes as a microreactor, the targeted screening was exploited by combining with HPLC-MS system. Four compounds confirmed as luteolin, luteoside, lonicerin, and isochlorogenic acid C and a fraction (M1) were screened out to be potent inhibitors of 5-LOX. Their anti-inflammatory activities were further investigated and confirmed by RAW 264.7 cells inflammation model and rat foot swelling model. Furthermore, M1 was prepared by MCI GEL CHP20P column chromatography, and further separated by Pre-HPLC. One new compound confirmed to be 5,7,3',4'-tetrahydroxyflavone-7-O-sambubioside was first isolated from LLJT. The results provide a new method for the effective separation of active components derived from natural products.HighlightsA 5-LOX mediated separation method was established for isolation of anti-inflammatory compounds.An anti-inflammatory ingredient was separated by MCI GEL CHP20P column chromatography.One new compound was first isolated from leaves of Lonicera japonica Thunb.5-LOX was immobilised on TiO2 nanotubes and exploited by combining with HPLC-MS system.The anti-inflammatory activity of screened components was evaluated. [Figure: see text].

Satisfaction in population-based cancer screening in a Chinese rural high-risk population: the Yangzhong early diagnosis and treatment of upper gastrointestinal cancer.

BACKGROUND: Screening for upper gastrointestinal cancer (UGC) effectively reduces morbidity and mortality in gastric and esophageal cancers. It is considered one of the effective measures for cancer control in China, but studies on its functional quality are lacking. Our study assessed the quality of screening service funded by Upper Gastrointestinal Cancer Early diagnosis and treatment (UGCEDAT) and its correlation in Yangzhong People's hospital, China. METHODS: A cross-sectional study was conducted among 516 screening users at a screening centre in Yanghzong People's hospital from April to July 2021. The service quality questionnaire (SERVQUAL) based on the service quality gap (SQG) model was adopted. We calculated the mean scores of perceptions and expectations and their gap. To determine the association between overall SQG and related features of participants, we used a multivariate logistic regression. RESULTS: The average scores of screening service users' perceptions and expectations were 4.05 and 4.55, respectively. The SQG of five dimensions (tangibles, reliability, responsiveness, assurance and empathy) were negative, and the overall SQG was -0.51. The responsiveness dimension had the largest gap, and tangibles had the smallest gap. Occupation status (AOR: 0.57; CI: 0.37-0.89), health self-assessment (AOR: 4.97; CI: 1.35-18.23), endoscopy experience (AOR: 0.55; CI: 0.38-0.81), distance from screening hospital (AOR: 1.85; CI: 1.25-2.73) and frequency of visit (AOR: 1.65; CI: 1.10-2.46) were associated with the overall SQG. CONCLUSIONS: We observed a negative gap between perceptions and expectations of the function quality of screening service, implying a high dissatisfaction across different dimensions. Service providers should take adequate measures to bridge the dimension with the largest quality gap. Meanwhile, attention should be paid to identifying the influencing factors of the overall SQG and the characteristics of dimensional expectations and perceptions to improve the effectiveness of the screening program.

A Dioscorea opposita Thunb Polysaccharide-Based Dual-Responsive Hydrogel for Insulin Controlled Release.

A novel hydrogel (DOP/PEI-PBA) based on the "three-component" reaction of 2-formylphenylboric acid (2-FPBA), the primary amine group of polyethyleneimine (PEI) and the cis-o-dihydroxy groups of Dioscorea opposita Thunb polysaccharide (DOP) was designed in this work. The hydrogel can be easily prepared by simply mixing the three reactants at room temperature. The hydrogel had dual responsiveness to glucose and pH, and can realize the controllable release of insulin. Moreover, the hydrogel combining insulin and DOP can inhibit the reactive oxygen species (ROS) level and malondialdehyde (MDA) content, and promote glucose consumption as well as the level of superoxide dismutase (SOD), in high-glucose-induced injury in HL-7702 cells, which reflects the synergistic effect of insulin and DOP to protect hepatocytes from oxidative stress at the same time. Further in vitro cytotoxicity studies showed that the hydrogel had good biocompatibility and no obvious toxicity to cells. These indicate that the prepared hydrogel (DOP/PEI-PBA) can be expected to be applied in the clinical treatment of insulin deficiency in diabetes.

Achyrophenols A-F: Polycyclic Polyphenol Lactone Skeletons and a Nor-Ursane-Type Triterpenoid from Achyrocline Satureioides.

Two novel derivatives of 2-methyl-1-(2,3,4,6-tetrahydroxyphenyl)propan-1-one and (S)-2-methyl-1-(2,3,4,6-tetrahydroxyphenyl)butan-1-one (1 and 2), four novel six-membered lactone phenols (3-6), and a nor-ursane type triterpenoid (7) named Achroacid, were isolated from the aerial part of Achyrocline satureioides. The absolute configurations of 1-7 are presented by spectroscopic data and X-ray crystallographic analysis. A DP4plus evaluation was applied to determine the final stereochemistry for 1 and 2. The biosynthesis pathway of 1 and 2 was proposed. 1 has potential on anti-Gram-negative bacteria. Both 1 and 2 exhibited a significant impact on anti-H1299 cells. Compounds 3-7 showed moderate cancer cell lethality and significant anti-inflammatory activities.

Serum level of lipoprotein-associated phospholipase A2 is a potential biomarker of vertebrobasilar dolichoectasia and its progression to cerebral infarction.

BACKGROUND: There are no effective therapies to prevent the occurrence and progression of vertebrobasilar dolichoectasia (VBD). In this study, we investigated the relationship between serum levels of lipoprotein-associated phospholipase A2 (Lp-PLA2) and the occurrence and progression of VBD. METHODS: Sixty (60) cases without VBD and ischemia stroke were considered as Group A, 100 cases with VBD were further divided into Group B (VBD without ischemic stroke, n = 54) and Group C (VBD with first ever acute posterior circulation ischemic stroke, n = 46). Demographic data (such as gender and age) and past medical history (such as hypertension, diabetes, and smoking history) were collected. The levels of serum low-density lipoprotein cholesterol (LDL-C), hypersensitivity C reactive protein (hs-CRP), glycosylated hemoglobin (HbAlc), homocysteine (HCY), uric acid (UA), fibrinogen (Fib), and Lp-PLA2, etc. were measured. Logistic regression analysis was used to assess the related factors of VBD and ischemic stroke secondary to VBD. RESULTS: Logistic multivariate regression analysis showed that only age and the level of serum Lp-PLA2 were significantly higher in group B than those in group A (P < 0.012, P < 0.001, respectively), however, only the level of serum Lp-PLA2 was significantly higher in group C than those in group B (P < 0.001). CONCLUSIONS: The serum marker Lp-PLA2 is an independent risk factor for the occurrence of VBD and the progression of VBD to posterior circulation ischemic stroke. Whether intervening on atherosclerosis could prevent the occurrence and development of VBD needs to be further studied.

Detoxification and activating blood circulation decoction reduces restenosis involving the TLR4/NF-κB pathway after balloon injury.

Restenosis is a major problem after percutaneous coronary intervention (PCI) treatment. Inflammation is one of the major core mechanisms involved in the occurrence of restenosis, and plays an important role in intimal hyperplasia. Detoxification and activating blood circulation decoction (DABCD) is a traditional Chinese medicine that is used in the treatment and prevention of atherosclerotic and inflammatory diseases. Our previous studies demonstrated that DABCD-mediated cardioprotection involves anti-inflammatory mechanisms and could be developed as a novel drug for the treatment of vascular smooth muscle cell (VSMC) proliferation and aortic restenosis. A rat model of postoperative restenosis after PCI was generated by balloon injury to determine the protective effects and potential mechanisms of DABCD. The injured segments of aortae were collected on days 14 and 28 after the operation to observe the morphological changes in the vascular structure and measure the proportion of inflammatory factors in plasma and vascular tissues, as well as test the proliferative activity of VSMCs. The expression of related proteins, namely, Toll-like receptor (TLR) 4 and nuclear factor (NF)-κB, in the mechanistic study was clarified by western blot analysis. We tested the hypothesis that the cardioprotective effects of DABCD on aortic restenosis are associated with the inhibition of aortic intimal hyperplasia in this model. Our results showed that DABCD has protective effect on rat aortic restenosis and the anti-inflammatory mechanism of DABCD on balloon-induced restenosis in rat may be due to its ability to inhibit TLR4-mediated NF-κB signaling pathways. DABCD may be a potential therapeutic agent against restenosis.

Severe Periodontitis Is Associated with Early-Onset Poststroke Depression Status.

BACKGROUND: Poststroke depression (PSD) is one of the most common complications after ischemic stroke, and periodontitis is associated with depression. However, whether severe periodontitis is associated with early-onset PSD status remains unknown. In this study, we aimed to investigate whether there is an association between severe periodontitis and PSD status in acute ischemic stroke patients. MATERIAL AND METHODS: We recruited 202 acute ischemic stroke patients within 7 days after stroke onset. Pocket depth and clinical attachment loss were assessed by oral examination to define the severe periodontitis. On the basis of diagnosis of PSD status according to DSM-5 criteria and a 24-item Hamilton Depression Rating Scale score greater than or equal to 8 within 2 weeks after stroke onset, we stratified patients into PSD status or non-PSD status groups and identified the independent predictors for the development of PSD status in multivariate logistic analysis. RESULTS: 77 (38.1%) patients were diagnosed as early-onset PSD status. PSD status group showed more severe periodontitis, lower income, lower Barthel Index (BI) score and Montreal Cognitive Assessment score, higher National Institutes of Health Stroke Scale score and modified Rankin scale (mRS) score compared with non-PSD status group. Multivariate logistic regression showed that severe periodontitis (odds ratio 2.401) and NIHSS score (>4, odds ratio 2.130) were independent predictors for early-onset PSD status. CONCLUSIONS: Severe periodontitis is found to be an important independent predictor of early-onset PSD status in patients with acute ischemic stroke, in addition to the well-known prognostic factors such as nonminor stroke assessed by NIHSS greater than 4.

Feasibility study on direct fermentation of soybean meal by Bacillus stearothermophilus under non-sterile conditions.

BACKGROUND: To evaluate the feasibility of high-temperature solid-state fermentation (SSF) using soybean meal (SBM) during the non-sterile process, Bacillus stearothermophilus was employed to assess the nutritional quality and bioactivity of SBM after fermentation. RESULTS: The fermented SBM (FSBM) without autoclaving showed significant improvements in nutritional quality and bioactivity. The contents of peptides and crude and soluble proteins increased by 131.21%, 5.3% and 15.52%, respectively. Meanwhile, DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging ability, reducing ability and hydroxyl free radical-scavenging activity rose by 57.07%, 238.92% and 368.26%, respectively. The inhibitory activity of angiotensin I-converting enzyme increased from 1.43 ± 0.83% to 26.89 ± 1.03%, while the trypsin inhibitor activity decreased by 74.05%. The contents of neutral and alkaline proteases and the growth of microorganisms in FSBM without autoclaving were higher and better than in steam-treated FSBM. After steam treatment, the water-holding capacity of SBM decreased, and a high crosslink density was observed on the surface of SBM particles. CONCLUSIONS: It is feasible to ferment SBM by high-temperature SSF using B. stearothermophilus under non-sterile conditions. Adverse effects of SSF using sterile SBM might be owing to the low water-holding capacity caused by autoclaving. © 2018 Society of Chemical Industry.

rSjP40 suppresses hepatic stellate cell activation by promoting microRNA-155 expression and inhibiting STAT5 and FOXO3a expression.

Activation of hepatic stellate cells (HSCs) is the central event of the evolution of hepatic fibrosis. Schistosomiasis is one of the pathogenic factors which could induce hepatic fibrosis. Previous studies have shown that recombinant Schistosoma japonicum egg antigen P40 (rSjP40) can inhibit the activation and proliferation of HSCs. MicroRNA-155 is one of the multifunctional noncoding RNA, which is involved in a series of important biological processes including cell development, proliferation, differentiation and apoptosis. Here, we try to observe the role of microRNA-155 in rSjP40-inhibited HSC activation and explore its potential mechanisms. We found that microRNA-155 was raised in rSjP40-treated HSCs, and further studies have shown that rSjP40 enhanced microRNA-155 expression by inhibiting STAT5 transcription. Up-regulated microRNA-155 can down-regulate the expression of FOXO3a and then participate in rSjP40-inhibited expression of α-smooth muscle actin (α-SMA) and collagen I. Furthermore, we observed microRNA-155 inhibitor could partially restore the down-regulation of FOXO3a, α-SMA and collagen I expression in LX-2 cells induced by rSjP40. Therefore, our research provides further insight into the mechanism by which rSjP40 could inhibit HSC activation via miR-155.

microRNA-146a is involved in rSjP40-inhibited activation of LX-2 cells by targeting Smad4 expression.

Previous studies have demonstrated that the recombinant Schistosoma japonicum protein P40 (rSjP40) could inhibit activation of hepatic stellate cells (HSCs) through the TGF-ß1/Smads signaling pathway. Since multiple microRNAs could play essential roles in HSC activation and in the process of hepatic fibrosis through targeting Smads, we attempted to seek the potential microRNAs that could be involved in rSjP40-induced inhibition of HSC activation. Using the method of quantitative real-time PCR, we found that rSjP40 could induce miR-146a expression in LX-2 cells. The down-regulated expression levels of Smad4 and α-SMA in LX-2 cells induced by rSjP40 were partially restored by an miR-146a inhibitor. miR-146a can be involved in rSjP40-induced inhibition of HSC activation through targeting Smad4. These findings provide us a new idea to explore the potential mechanisms by which rSjP40 could regulate the process of hepatic fibrosis.